Speaker embeddings learnt by deep neural networks have shown very impressive performance for speaker recognition (SR)[1]}, [2]}, [3]}. It has been extensively verified that neural models with deeper residual layers (such as ResNet[4]}, [2]}) and more complex neural blocks (such as ECAPA-TDNN[3]}) can significantly outperform the classical x-vector[1]} extractor.
However, these models are computationally demanding. It is difficult, if not impossible, to deploy them for resource-constrained applications that have limited computational budgets and require low real time factor.
